Introduction Immune agents including anti-programmed death receptor-1 and anti-cytotoxic T-lymphocyte antigen-4 have been associated with numerous immune-related complications. Pembrolizumab, a programmed death-1 inhibitor, has been associated with a number of immune-related adverse events such as pneumonitis, colitis, hepatitis, hypophysitis, hyperthyroidism, hypothyroidism, nephritis, and type 1 diabetes. Case report We present a rare case of an elderly male on pembrolizumab who suffered from four autoimmune toxicities including type 1 diabetes, pneumonitis, hypothyroidism, and polymyalgia rheumatica likely catalyzed by age-related immune activation. Management and outcome: Immunotherapy was indefinitely stopped, and patient was started on steroids for the immune-related adverse events with complete resolution of polymyalgia rheumatica. Thyroid dysfunction resolved once he started thyroid replacement therapy. His diabetes is well controlled with insulin and is followed by endocrinology. He continues on prednisone for immune-mediated pneumonitis with a good response with regular monitoring via computed tomography scans and pulmonary consultation. Discussion Few cases wherein multiple toxicities are seen within one patient are reported. Aging appears to be a risk factor for immune-related adverse events. Aging is associated with an increased incidence of autoimmunity as programmed death-1 ligand expression represents an important mechanism that tissues use to protect from self-reactive effector T cells. Programmed death-1 blockade breaks this protective mechanism and enhances autoimmune diseases. Therefore, close monitoring and extreme vigilance is warranted while using immune checkpoint inhibitors including pembrolizumab as multiple toxicities can occur within a short span of infusion, especially in elderly individuals. Prompt discontinuation and the use of a multidisciplinary team are prudent to prevent further morbidity and mortality.

Background: The concept of classification of clinical data can be utilized in the development of an effective diagnosis system by taking the advantage of computational intelligence. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important problem in neural networks. Unfortunately, although several classification studies have been carried out with significant performance, many of the current methods often fail to reach out to patients. Graphical user interface-enabled tools need to be developed through which medical practitioners can simply enter the health profiles of their patients and receive an instant diabetes prediction with an acceptable degree of confidence. Methods: In this study, the neural network approach was used for a dataset of 768 persons from a Pima Indian population living near Phoenix, AZ. A neural network mixture of experts model was trained with these data using the expectation-minimization algorithm. Results: The mixture of experts method was used to train the algorithm with 97% accuracy. A graphical user interface was developed that would work in conjunction with the trained network to provide the output in a presentable format. Conclusions: This study provides a machine-implementable approach that can be used by physicians and patients to minimize the extent of error in diagnosis. The authors are hopeful that replication of results of this study in other populations may lead to improved diagnosis. Physicians can simply enter the health profile of patients and get the diagnosis for diabetes type 2.

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Results illustrate the need for better promotion and integration of clinical guidelines with antibiograms when developing antibiotic education programs for residents in training. In addition, pediatric hospitalists should play an active role in the implementation of these programs and can provide valuable insight into the development of educational programs in conjunction with graduate medical education divisions.

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